PROJECT SUMMARY Human geneticists have used genome wide association studies (GWAS) to identify genetic loci associated with complex diseases, including cardiovascular disease (CVD). However, it remains challenging to uncover their functional effects, particularly because most GWAS variants are found in noncoding regions. To test the hypothesis that GWAS variants affect disease by changing gene expression levels, my laboratory and others have investigated the overlap between GWAS loci and expression quantitative trait loci (eQTLs). Although this approach revealed many GWAS variants that affect traits through changes in gene expression, most GWAS associations are not eQTLs. These associations could be explained by mechanisms independent from gene expression levels, including isoform level variation, mRNA decay, and translation efficiency. Alternative polyadenylation (APA) leads to isoform level variation, and can potentially affect the rate of mRNA decay or translation. Therefore, APA is one potential mechanism to explain how GWAS loci contribute to complex traits, including CVD. CVD includes a wide range of complex cardiovascular traits that together are the leading cause of death worldwide. With many unexplained, but well replicated, GWAS hits, this is a tractable system to test for overlap between variation associated with APA and CVD. I propose to study APA in a faithful cell culture model for CVD, induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). I will culture and differentiate a panel of 70 Yoruba iPSC lines, collected in the HapMap project and reprogrammed into IPSCs in the Gilad lab. In Aim 1, I will collect the 3? end of mRNA collected from whole cells and nuclear fractions of iPSC-CMs for 3? sequencing (3? Seq). Studying mRNA from whole cells and the nucleus separately will allow me to decouple the effects of APA from nuclear export and mRNA decay. In Aim 2, I will use an innovative quantitative trait locus (QTL) approach to detect genetic variation associated with APA variation (ApaQTLs). Finally, in Aim 3, I will overlap ApaQTLs with CVD GWAS variants to investigate the contribution of APA as a gene regulatory mechanism modulating CVD risk in comparison to variation in gene expression levels alone. This work will elucidate novel candidate genes and potential therapeutic targets for CVD. More broadly, my work will deepen our understanding of the specific genetic mechanisms that underlie complex traits, independent of previously explored mechanisms. Importantly, the proposed research provides opportunities for training in molecular biology, statistical genetics, and bioinformatics.